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skills/torch_geometric/references/transforms_reference.md
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skills/torch_geometric/references/transforms_reference.md
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# PyTorch Geometric Transforms Reference
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This document provides a comprehensive reference of all transforms available in `torch_geometric.transforms`.
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## Overview
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Transforms modify `Data` or `HeteroData` objects before or during training. Apply them via:
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```python
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# During dataset loading
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dataset = MyDataset(root='/tmp', transform=MyTransform())
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# Apply to individual data
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transform = MyTransform()
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data = transform(data)
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# Compose multiple transforms
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from torch_geometric.transforms import Compose
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transform = Compose([Transform1(), Transform2(), Transform3()])
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```
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## General Transforms
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### NormalizeFeatures
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**Purpose**: Row-normalizes node features to sum to 1
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**Use case**: Feature scaling, probability-like features
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```python
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from torch_geometric.transforms import NormalizeFeatures
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transform = NormalizeFeatures()
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```
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### ToDevice
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**Purpose**: Transfers data to specified device (CPU/GPU)
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**Use case**: GPU training, device management
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```python
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from torch_geometric.transforms import ToDevice
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transform = ToDevice('cuda')
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```
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### RandomNodeSplit
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**Purpose**: Creates train/val/test node masks
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**Use case**: Node classification splits
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**Parameters**: `split='train_rest'`, `num_splits`, `num_val`, `num_test`
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```python
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from torch_geometric.transforms import RandomNodeSplit
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transform = RandomNodeSplit(num_val=0.1, num_test=0.2)
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```
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### RandomLinkSplit
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**Purpose**: Creates train/val/test edge splits
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**Use case**: Link prediction
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**Parameters**: `num_val`, `num_test`, `is_undirected`, `split_labels`
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```python
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from torch_geometric.transforms import RandomLinkSplit
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transform = RandomLinkSplit(num_val=0.1, num_test=0.2)
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```
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### IndexToMask
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**Purpose**: Converts indices to boolean masks
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**Use case**: Data preprocessing
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```python
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from torch_geometric.transforms import IndexToMask
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transform = IndexToMask()
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```
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### MaskToIndex
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**Purpose**: Converts boolean masks to indices
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**Use case**: Data preprocessing
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```python
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from torch_geometric.transforms import MaskToIndex
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transform = MaskToIndex()
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```
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### FixedPoints
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**Purpose**: Samples a fixed number of points
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**Use case**: Point cloud subsampling
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**Parameters**: `num`, `replace`, `allow_duplicates`
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```python
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from torch_geometric.transforms import FixedPoints
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transform = FixedPoints(1024)
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```
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### ToDense
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**Purpose**: Converts to dense adjacency matrices
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**Use case**: Small graphs, dense operations
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```python
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from torch_geometric.transforms import ToDense
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transform = ToDense(num_nodes=100)
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```
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### ToSparseTensor
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**Purpose**: Converts edge_index to SparseTensor
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**Use case**: Efficient sparse operations
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**Parameters**: `remove_edge_index`, `fill_cache`
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```python
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from torch_geometric.transforms import ToSparseTensor
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transform = ToSparseTensor()
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```
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## Graph Structure Transforms
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### ToUndirected
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**Purpose**: Converts directed graph to undirected
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**Use case**: Undirected graph algorithms
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**Parameters**: `reduce='add'` (how to handle duplicate edges)
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```python
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from torch_geometric.transforms import ToUndirected
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transform = ToUndirected()
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```
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### AddSelfLoops
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**Purpose**: Adds self-loops to all nodes
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**Use case**: GCN-style convolutions
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**Parameters**: `fill_value` (edge attribute for self-loops)
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```python
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from torch_geometric.transforms import AddSelfLoops
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transform = AddSelfLoops()
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```
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### RemoveSelfLoops
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**Purpose**: Removes all self-loops
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**Use case**: Cleaning graph structure
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```python
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from torch_geometric.transforms import RemoveSelfLoops
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transform = RemoveSelfLoops()
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```
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### RemoveIsolatedNodes
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**Purpose**: Removes nodes without edges
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**Use case**: Graph cleaning
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```python
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from torch_geometric.transforms import RemoveIsolatedNodes
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transform = RemoveIsolatedNodes()
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```
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### RemoveDuplicatedEdges
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**Purpose**: Removes duplicate edges
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**Use case**: Graph cleaning
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```python
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from torch_geometric.transforms import RemoveDuplicatedEdges
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transform = RemoveDuplicatedEdges()
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```
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### LargestConnectedComponents
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**Purpose**: Keeps only the largest connected component
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**Use case**: Focus on main graph structure
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**Parameters**: `num_components` (how many components to keep)
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```python
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from torch_geometric.transforms import LargestConnectedComponents
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transform = LargestConnectedComponents(num_components=1)
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```
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### KNNGraph
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**Purpose**: Creates edges based on k-nearest neighbors
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**Use case**: Point clouds, spatial data
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**Parameters**: `k`, `loop`, `force_undirected`, `flow`
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```python
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from torch_geometric.transforms import KNNGraph
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transform = KNNGraph(k=6)
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```
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### RadiusGraph
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**Purpose**: Creates edges within a radius
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**Use case**: Point clouds, spatial data
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**Parameters**: `r`, `loop`, `max_num_neighbors`, `flow`
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```python
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from torch_geometric.transforms import RadiusGraph
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transform = RadiusGraph(r=0.1)
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```
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### Delaunay
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**Purpose**: Computes Delaunay triangulation
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**Use case**: 2D/3D spatial graphs
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```python
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from torch_geometric.transforms import Delaunay
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transform = Delaunay()
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```
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### FaceToEdge
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**Purpose**: Converts mesh faces to edges
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**Use case**: Mesh processing
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```python
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from torch_geometric.transforms import FaceToEdge
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transform = FaceToEdge()
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```
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### LineGraph
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**Purpose**: Converts graph to its line graph
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**Use case**: Edge-centric analysis
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**Parameters**: `force_directed`
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```python
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from torch_geometric.transforms import LineGraph
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transform = LineGraph()
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```
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### GDC
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**Purpose**: Graph Diffusion Convolution preprocessing
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**Use case**: Improved message passing
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**Parameters**: `self_loop_weight`, `normalization_in`, `normalization_out`, `diffusion_kwargs`
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```python
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from torch_geometric.transforms import GDC
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transform = GDC(self_loop_weight=1, normalization_in='sym',
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diffusion_kwargs=dict(method='ppr', alpha=0.15))
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```
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### SIGN
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**Purpose**: Scalable Inception Graph Neural Networks preprocessing
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**Use case**: Efficient multi-scale features
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**Parameters**: `K` (number of hops)
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```python
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from torch_geometric.transforms import SIGN
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transform = SIGN(K=3)
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```
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## Feature Transforms
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### OneHotDegree
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**Purpose**: One-hot encodes node degree
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**Use case**: Degree as feature
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**Parameters**: `max_degree`, `cat` (concatenate with existing features)
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```python
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from torch_geometric.transforms import OneHotDegree
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transform = OneHotDegree(max_degree=100)
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```
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### LocalDegreeProfile
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**Purpose**: Appends local degree profile
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**Use case**: Structural node features
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```python
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from torch_geometric.transforms import LocalDegreeProfile
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transform = LocalDegreeProfile()
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```
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### Constant
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**Purpose**: Adds constant features to nodes
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**Use case**: Featureless graphs
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**Parameters**: `value`, `cat`
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```python
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from torch_geometric.transforms import Constant
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transform = Constant(value=1.0)
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```
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### TargetIndegree
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**Purpose**: Saves in-degree as target
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**Use case**: Degree prediction
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**Parameters**: `norm`, `max_value`
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```python
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from torch_geometric.transforms import TargetIndegree
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transform = TargetIndegree(norm=False)
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```
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### AddRandomWalkPE
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**Purpose**: Adds random walk positional encoding
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**Use case**: Positional information
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**Parameters**: `walk_length`, `attr_name`
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```python
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from torch_geometric.transforms import AddRandomWalkPE
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transform = AddRandomWalkPE(walk_length=20)
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```
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### AddLaplacianEigenvectorPE
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**Purpose**: Adds Laplacian eigenvector positional encoding
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**Use case**: Spectral positional information
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**Parameters**: `k` (number of eigenvectors), `attr_name`
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```python
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from torch_geometric.transforms import AddLaplacianEigenvectorPE
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transform = AddLaplacianEigenvectorPE(k=10)
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```
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### AddMetaPaths
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**Purpose**: Adds meta-path induced edges
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**Use case**: Heterogeneous graphs
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**Parameters**: `metapaths`, `drop_orig_edges`, `drop_unconnected_nodes`
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```python
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from torch_geometric.transforms import AddMetaPaths
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metapaths = [[('author', 'paper'), ('paper', 'author')]] # Co-authorship
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transform = AddMetaPaths(metapaths)
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```
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### SVDFeatureReduction
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**Purpose**: Reduces feature dimensionality via SVD
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**Use case**: Dimensionality reduction
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**Parameters**: `out_channels`
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```python
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from torch_geometric.transforms import SVDFeatureReduction
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transform = SVDFeatureReduction(out_channels=64)
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```
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## Vision/Spatial Transforms
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### Center
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**Purpose**: Centers node positions
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**Use case**: Point cloud preprocessing
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```python
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from torch_geometric.transforms import Center
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transform = Center()
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```
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### NormalizeScale
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**Purpose**: Normalizes positions to unit sphere
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**Use case**: Point cloud normalization
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```python
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from torch_geometric.transforms import NormalizeScale
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transform = NormalizeScale()
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```
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### NormalizeRotation
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**Purpose**: Rotates to principal components
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**Use case**: Rotation-invariant learning
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**Parameters**: `max_points`
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```python
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from torch_geometric.transforms import NormalizeRotation
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transform = NormalizeRotation()
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```
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### Distance
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**Purpose**: Saves Euclidean distance as edge attribute
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**Use case**: Spatial graphs
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**Parameters**: `norm`, `max_value`, `cat`
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```python
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from torch_geometric.transforms import Distance
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transform = Distance(norm=False, cat=False)
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```
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### Cartesian
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**Purpose**: Saves relative Cartesian coordinates as edge attributes
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**Use case**: Spatial relationships
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**Parameters**: `norm`, `max_value`, `cat`
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```python
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from torch_geometric.transforms import Cartesian
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transform = Cartesian(norm=False)
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```
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### Polar
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**Purpose**: Saves polar coordinates as edge attributes
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**Use case**: 2D spatial graphs
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**Parameters**: `norm`, `max_value`, `cat`
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```python
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from torch_geometric.transforms import Polar
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transform = Polar(norm=False)
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```
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### Spherical
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**Purpose**: Saves spherical coordinates as edge attributes
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**Use case**: 3D spatial graphs
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**Parameters**: `norm`, `max_value`, `cat`
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```python
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from torch_geometric.transforms import Spherical
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transform = Spherical(norm=False)
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```
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### LocalCartesian
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**Purpose**: Saves coordinates in local coordinate system
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**Use case**: Local spatial features
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**Parameters**: `norm`, `cat`
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```python
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from torch_geometric.transforms import LocalCartesian
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transform = LocalCartesian()
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```
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### PointPairFeatures
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**Purpose**: Computes point pair features
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**Use case**: 3D registration, correspondence
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**Parameters**: `cat`
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```python
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from torch_geometric.transforms import PointPairFeatures
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transform = PointPairFeatures()
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```
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## Data Augmentation
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### RandomJitter
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**Purpose**: Randomly jitters node positions
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**Use case**: Point cloud augmentation
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**Parameters**: `translate`, `scale`
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```python
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from torch_geometric.transforms import RandomJitter
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transform = RandomJitter(0.01)
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```
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### RandomFlip
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**Purpose**: Randomly flips positions along axis
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**Use case**: Geometric augmentation
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**Parameters**: `axis`, `p` (probability)
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```python
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from torch_geometric.transforms import RandomFlip
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transform = RandomFlip(axis=0, p=0.5)
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```
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### RandomScale
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**Purpose**: Randomly scales positions
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**Use case**: Scale augmentation
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**Parameters**: `scales` (min, max)
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```python
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from torch_geometric.transforms import RandomScale
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transform = RandomScale((0.9, 1.1))
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```
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### RandomRotate
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**Purpose**: Randomly rotates positions
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**Use case**: Rotation augmentation
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**Parameters**: `degrees` (range), `axis` (rotation axis)
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```python
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from torch_geometric.transforms import RandomRotate
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transform = RandomRotate(degrees=15, axis=2)
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```
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### RandomShear
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**Purpose**: Randomly shears positions
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**Use case**: Geometric augmentation
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**Parameters**: `shear` (range)
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```python
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from torch_geometric.transforms import RandomShear
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transform = RandomShear(0.1)
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```
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### RandomTranslate
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**Purpose**: Randomly translates positions
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**Use case**: Translation augmentation
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**Parameters**: `translate` (range)
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```python
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from torch_geometric.transforms import RandomTranslate
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transform = RandomTranslate(0.1)
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```
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### LinearTransformation
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**Purpose**: Applies linear transformation matrix
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**Use case**: Custom geometric transforms
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**Parameters**: `matrix`
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```python
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from torch_geometric.transforms import LinearTransformation
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import torch
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matrix = torch.eye(3)
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transform = LinearTransformation(matrix)
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```
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## Mesh Processing
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### SamplePoints
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**Purpose**: Samples points uniformly from mesh
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**Use case**: Mesh to point cloud conversion
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**Parameters**: `num`, `remove_faces`, `include_normals`
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```python
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from torch_geometric.transforms import SamplePoints
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transform = SamplePoints(num=1024)
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```
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### GenerateMeshNormals
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**Purpose**: Generates face/vertex normals
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**Use case**: Mesh processing
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```python
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from torch_geometric.transforms import GenerateMeshNormals
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transform = GenerateMeshNormals()
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```
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### FaceToEdge
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**Purpose**: Converts mesh faces to edges
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**Use case**: Mesh to graph conversion
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**Parameters**: `remove_faces`
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```python
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from torch_geometric.transforms import FaceToEdge
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transform = FaceToEdge()
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```
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## Sampling and Splitting
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### GridSampling
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**Purpose**: Clusters points in voxel grid
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**Use case**: Point cloud downsampling
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**Parameters**: `size` (voxel size), `start`, `end`
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```python
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from torch_geometric.transforms import GridSampling
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transform = GridSampling(size=0.1)
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```
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### FixedPoints
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**Purpose**: Samples fixed number of points
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**Use case**: Uniform point cloud size
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**Parameters**: `num`, `replace`, `allow_duplicates`
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```python
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from torch_geometric.transforms import FixedPoints
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transform = FixedPoints(num=2048, replace=False)
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```
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### RandomScale
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**Purpose**: Randomly scales by sampling from range
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**Use case**: Scale augmentation (already listed above)
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### VirtualNode
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||||
**Purpose**: Adds a virtual node connected to all nodes
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||||
**Use case**: Global information propagation
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||||
```python
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from torch_geometric.transforms import VirtualNode
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transform = VirtualNode()
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```
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## Specialized Transforms
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### ToSLIC
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||||
**Purpose**: Converts images to superpixel graphs (SLIC algorithm)
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||||
**Use case**: Image as graph
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||||
**Parameters**: `num_segments`, `compactness`, `add_seg`, `add_img`
|
||||
```python
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from torch_geometric.transforms import ToSLIC
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transform = ToSLIC(num_segments=75)
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```
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||||
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||||
### GCNNorm
|
||||
**Purpose**: Applies GCN-style normalization to edges
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||||
**Use case**: Preprocessing for GCN
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||||
**Parameters**: `add_self_loops`
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||||
```python
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from torch_geometric.transforms import GCNNorm
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transform = GCNNorm(add_self_loops=True)
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||||
```
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||||
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||||
### LaplacianLambdaMax
|
||||
**Purpose**: Computes largest Laplacian eigenvalue
|
||||
**Use case**: ChebConv preprocessing
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||||
**Parameters**: `normalization`, `is_undirected`
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||||
```python
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||||
from torch_geometric.transforms import LaplacianLambdaMax
|
||||
transform = LaplacianLambdaMax(normalization='sym')
|
||||
```
|
||||
|
||||
### NormalizeRotation
|
||||
**Purpose**: Rotates mesh/point cloud to align with principal axes
|
||||
**Use case**: Canonical orientation
|
||||
**Parameters**: `max_points`
|
||||
```python
|
||||
from torch_geometric.transforms import NormalizeRotation
|
||||
transform = NormalizeRotation()
|
||||
```
|
||||
|
||||
## Compose and Apply
|
||||
|
||||
### Compose
|
||||
**Purpose**: Chains multiple transforms
|
||||
**Use case**: Complex preprocessing pipelines
|
||||
```python
|
||||
from torch_geometric.transforms import Compose
|
||||
transform = Compose([
|
||||
Center(),
|
||||
NormalizeScale(),
|
||||
KNNGraph(k=6),
|
||||
Distance(norm=False),
|
||||
])
|
||||
```
|
||||
|
||||
### BaseTransform
|
||||
**Purpose**: Base class for custom transforms
|
||||
**Use case**: Implementing custom transforms
|
||||
```python
|
||||
from torch_geometric.transforms import BaseTransform
|
||||
|
||||
class MyTransform(BaseTransform):
|
||||
def __init__(self, param):
|
||||
self.param = param
|
||||
|
||||
def __call__(self, data):
|
||||
# Modify data
|
||||
data.x = data.x * self.param
|
||||
return data
|
||||
```
|
||||
|
||||
## Common Transform Combinations
|
||||
|
||||
### Node Classification Preprocessing
|
||||
```python
|
||||
transform = Compose([
|
||||
NormalizeFeatures(),
|
||||
RandomNodeSplit(num_val=0.1, num_test=0.2),
|
||||
])
|
||||
```
|
||||
|
||||
### Point Cloud Processing
|
||||
```python
|
||||
transform = Compose([
|
||||
Center(),
|
||||
NormalizeScale(),
|
||||
RandomRotate(degrees=15, axis=2),
|
||||
RandomJitter(0.01),
|
||||
KNNGraph(k=6),
|
||||
Distance(norm=False),
|
||||
])
|
||||
```
|
||||
|
||||
### Mesh to Graph
|
||||
```python
|
||||
transform = Compose([
|
||||
FaceToEdge(remove_faces=True),
|
||||
GenerateMeshNormals(),
|
||||
Distance(norm=True),
|
||||
])
|
||||
```
|
||||
|
||||
### Graph Structure Enhancement
|
||||
```python
|
||||
transform = Compose([
|
||||
ToUndirected(),
|
||||
AddSelfLoops(),
|
||||
RemoveIsolatedNodes(),
|
||||
GCNNorm(),
|
||||
])
|
||||
```
|
||||
|
||||
### Heterogeneous Graph Preprocessing
|
||||
```python
|
||||
transform = Compose([
|
||||
AddMetaPaths(metapaths=[
|
||||
[('author', 'paper'), ('paper', 'author')],
|
||||
[('author', 'paper'), ('paper', 'conference'), ('conference', 'paper'), ('paper', 'author')]
|
||||
]),
|
||||
RandomNodeSplit(split='train_rest', num_val=0.1, num_test=0.2),
|
||||
])
|
||||
```
|
||||
|
||||
### Link Prediction
|
||||
```python
|
||||
transform = Compose([
|
||||
NormalizeFeatures(),
|
||||
RandomLinkSplit(num_val=0.1, num_test=0.2, is_undirected=True),
|
||||
])
|
||||
```
|
||||
|
||||
## Usage Tips
|
||||
|
||||
1. **Order matters**: Apply structural transforms before feature transforms
|
||||
2. **Caching**: Some transforms (like GDC) are expensive—apply once
|
||||
3. **Augmentation**: Use Random* transforms during training only
|
||||
4. **Compose sparingly**: Too many transforms slow down data loading
|
||||
5. **Custom transforms**: Inherit from `BaseTransform` for custom logic
|
||||
6. **Pre-transforms**: Apply expensive transforms once during dataset processing:
|
||||
```python
|
||||
dataset = MyDataset(root='/tmp', pre_transform=ExpensiveTransform())
|
||||
```
|
||||
7. **Dynamic transforms**: Apply cheap transforms during training:
|
||||
```python
|
||||
dataset = MyDataset(root='/tmp', transform=CheapTransform())
|
||||
```
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
**Expensive transforms** (apply as pre_transform):
|
||||
- GDC
|
||||
- SIGN
|
||||
- KNNGraph (for large point clouds)
|
||||
- AddLaplacianEigenvectorPE
|
||||
- SVDFeatureReduction
|
||||
|
||||
**Cheap transforms** (apply as transform):
|
||||
- NormalizeFeatures
|
||||
- ToUndirected
|
||||
- AddSelfLoops
|
||||
- Random* augmentations
|
||||
- ToDevice
|
||||
|
||||
**Example**:
|
||||
```python
|
||||
from torch_geometric.datasets import Planetoid
|
||||
from torch_geometric.transforms import Compose, GDC, NormalizeFeatures
|
||||
|
||||
# Expensive preprocessing done once
|
||||
pre_transform = GDC(
|
||||
self_loop_weight=1,
|
||||
normalization_in='sym',
|
||||
diffusion_kwargs=dict(method='ppr', alpha=0.15)
|
||||
)
|
||||
|
||||
# Cheap transform applied each time
|
||||
transform = NormalizeFeatures()
|
||||
|
||||
dataset = Planetoid(
|
||||
root='/tmp/Cora',
|
||||
name='Cora',
|
||||
pre_transform=pre_transform,
|
||||
transform=transform
|
||||
)
|
||||
```
|
||||
Reference in New Issue
Block a user